Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.9 KiB
Average record size in memory347.1 B

Variable types

Text2
Categorical1
Numeric17

Alerts

week_start_date has constant value "2023-10-02" Constant
avg_heart_rate is highly overall correlated with avg_pace_min_per_km and 14 other fieldsHigh correlation
avg_pace_min_per_km is highly overall correlated with avg_heart_rate and 15 other fieldsHigh correlation
cycling_distance_km is highly overall correlated with avg_heart_rate and 15 other fieldsHigh correlation
exercise_sessions is highly overall correlated with avg_heart_rate and 15 other fieldsHigh correlation
floors_climbed is highly overall correlated with avg_heart_rate and 15 other fieldsHigh correlation
max_heart_rate is highly overall correlated with avg_heart_rate and 15 other fieldsHigh correlation
min_heart_rate is highly overall correlated with avg_heart_rate and 14 other fieldsHigh correlation
move_minutes is highly overall correlated with avg_heart_rate and 15 other fieldsHigh correlation
running_distance_km is highly overall correlated with avg_heart_rate and 14 other fieldsHigh correlation
sedentary_minutes is highly overall correlated with avg_heart_rate and 14 other fieldsHigh correlation
sleep_hours_total is highly overall correlated with avg_heart_rate and 14 other fieldsHigh correlation
stress_level_avg is highly overall correlated with avg_heart_rate and 15 other fieldsHigh correlation
total_active_minutes is highly overall correlated with avg_heart_rate and 15 other fieldsHigh correlation
total_calories_burned is highly overall correlated with avg_heart_rate and 15 other fieldsHigh correlation
total_distance_km is highly overall correlated with avg_heart_rate and 14 other fieldsHigh correlation
total_steps is highly overall correlated with avg_heart_rate and 14 other fieldsHigh correlation
walking_distance_km is highly overall correlated with avg_pace_min_per_km and 8 other fieldsHigh correlation
user_id has unique values Unique
cycling_distance_km has 17 (34.0%) zeros Zeros
running_distance_km has 3 (6.0%) zeros Zeros

Reproduction

Analysis started2025-06-03 14:24:53.489716
Analysis finished2025-06-03 14:25:10.703576
Duration17.21 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

user_id
Text

Unique 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.2 KiB
2025-06-03T16:25:10.835799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters300
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowUSR001
2nd rowUSR002
3rd rowUSR003
4th rowUSR004
5th rowUSR005
ValueCountFrequency (%)
usr001 1
 
2.0%
usr002 1
 
2.0%
usr003 1
 
2.0%
usr004 1
 
2.0%
usr005 1
 
2.0%
usr006 1
 
2.0%
usr007 1
 
2.0%
usr008 1
 
2.0%
usr009 1
 
2.0%
usr010 1
 
2.0%
Other values (40) 40
80.0%
2025-06-03T16:25:11.072915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 64
21.3%
U 50
16.7%
S 50
16.7%
R 50
16.7%
1 15
 
5.0%
2 15
 
5.0%
3 15
 
5.0%
4 15
 
5.0%
5 6
 
2.0%
6 5
 
1.7%
Other values (3) 15
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 64
21.3%
U 50
16.7%
S 50
16.7%
R 50
16.7%
1 15
 
5.0%
2 15
 
5.0%
3 15
 
5.0%
4 15
 
5.0%
5 6
 
2.0%
6 5
 
1.7%
Other values (3) 15
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 64
21.3%
U 50
16.7%
S 50
16.7%
R 50
16.7%
1 15
 
5.0%
2 15
 
5.0%
3 15
 
5.0%
4 15
 
5.0%
5 6
 
2.0%
6 5
 
1.7%
Other values (3) 15
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 64
21.3%
U 50
16.7%
S 50
16.7%
R 50
16.7%
1 15
 
5.0%
2 15
 
5.0%
3 15
 
5.0%
4 15
 
5.0%
5 6
 
2.0%
6 5
 
1.7%
Other values (3) 15
 
5.0%

week_start_date
Categorical

Constant 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2023-10-02
50 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters500
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-02
2nd row2023-10-02
3rd row2023-10-02
4th row2023-10-02
5th row2023-10-02

Common Values

ValueCountFrequency (%)
2023-10-02 50
100.0%

Length

2025-06-03T16:25:11.169364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-03T16:25:11.259001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-02 50
100.0%

Most occurring characters

ValueCountFrequency (%)
2 150
30.0%
0 150
30.0%
- 100
20.0%
3 50
 
10.0%
1 50
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 150
30.0%
0 150
30.0%
- 100
20.0%
3 50
 
10.0%
1 50
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 150
30.0%
0 150
30.0%
- 100
20.0%
3 50
 
10.0%
1 50
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 150
30.0%
0 150
30.0%
- 100
20.0%
3 50
 
10.0%
1 50
 
10.0%

total_steps
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48535.4
Minimum16840
Maximum75280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:11.345068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum16840
5-th percentile19334
Q140200
median48920
Q363350
95-th percentile73140
Maximum75280
Range58440
Interquartile range (IQR)23150

Descriptive statistics

Standard deviation17170.435
Coefficient of variation (CV)0.35377138
Kurtosis-0.90851641
Mean48535.4
Median Absolute Deviation (MAD)13290
Skewness-0.27309584
Sum2426770
Variance2.9482385 × 108
MonotonicityNot monotonic
2025-06-03T16:25:11.472726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
52840 2
 
4.0%
45680 2
 
4.0%
24680 2
 
4.0%
61580 1
 
2.0%
18920 1
 
2.0%
68720 1
 
2.0%
28340 1
 
2.0%
48560 1
 
2.0%
72480 1
 
2.0%
69340 1
 
2.0%
Other values (37) 37
74.0%
ValueCountFrequency (%)
16840 1
2.0%
18420 1
2.0%
18920 1
2.0%
19840 1
2.0%
21840 1
2.0%
22340 1
2.0%
24680 2
4.0%
26480 1
2.0%
27680 1
2.0%
28340 1
2.0%
ValueCountFrequency (%)
75280 1
2.0%
74280 1
2.0%
73680 1
2.0%
72480 1
2.0%
71520 1
2.0%
69840 1
2.0%
69340 1
2.0%
68720 1
2.0%
68420 1
2.0%
67840 1
2.0%

total_distance_km
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.99
Minimum11.8
Maximum52.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:11.633432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11.8
5-th percentile13.515
Q128.15
median34.25
Q344.375
95-th percentile51.195
Maximum52.7
Range40.9
Interquartile range (IQR)16.225

Descriptive statistics

Standard deviation12.024231
Coefficient of variation (CV)0.35375791
Kurtosis-0.90861079
Mean33.99
Median Absolute Deviation (MAD)9.3
Skewness-0.27615883
Sum1699.5
Variance144.58214
MonotonicityNot monotonic
2025-06-03T16:25:11.755630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
34 2
 
4.0%
32 2
 
4.0%
17.3 2
 
4.0%
19.8 1
 
2.0%
43.1 1
 
2.0%
48.1 1
 
2.0%
37.2 1
 
2.0%
50.7 1
 
2.0%
13.2 1
 
2.0%
48.5 1
 
2.0%
Other values (37) 37
74.0%
ValueCountFrequency (%)
11.8 1
2.0%
12.9 1
2.0%
13.2 1
2.0%
13.9 1
2.0%
15.3 1
2.0%
15.6 1
2.0%
17.3 2
4.0%
18.5 1
2.0%
19.4 1
2.0%
19.8 1
2.0%
ValueCountFrequency (%)
52.7 1
2.0%
52 1
2.0%
51.6 1
2.0%
50.7 1
2.0%
50.1 1
2.0%
48.9 1
2.0%
48.5 1
2.0%
48.1 1
2.0%
47.9 1
2.0%
47.5 1
2.0%

total_calories_burned
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2327
Minimum1180
Maximum3250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:11.883240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1180
5-th percentile1298
Q11995
median2365
Q32880
95-th percentile3180
Maximum3250
Range2070
Interquartile range (IQR)885

Descriptive statistics

Standard deviation620.33648
Coefficient of variation (CV)0.26658207
Kurtosis-0.93960141
Mean2327
Median Absolute Deviation (MAD)465
Skewness-0.34484212
Sum116350
Variance384817.35
MonotonicityNot monotonic
2025-06-03T16:25:12.269445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2480 3
 
6.0%
2890 2
 
4.0%
2650 2
 
4.0%
3120 2
 
4.0%
2140 2
 
4.0%
3180 2
 
4.0%
1380 2
 
4.0%
2280 1
 
2.0%
1280 1
 
2.0%
1650 1
 
2.0%
Other values (32) 32
64.0%
ValueCountFrequency (%)
1180 1
2.0%
1240 1
2.0%
1280 1
2.0%
1320 1
2.0%
1350 1
2.0%
1380 2
4.0%
1420 1
2.0%
1480 1
2.0%
1520 1
2.0%
1650 1
2.0%
ValueCountFrequency (%)
3250 1
2.0%
3220 1
2.0%
3180 2
4.0%
3120 2
4.0%
3080 1
2.0%
3050 1
2.0%
3040 1
2.0%
2980 1
2.0%
2920 1
2.0%
2890 2
4.0%

total_active_minutes
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284.9
Minimum75
Maximum485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:12.374718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile89.5
Q1210
median285
Q3392.5
95-th percentile470.5
Maximum485
Range410
Interquartile range (IQR)182.5

Descriptive statistics

Standard deviation120.63814
Coefficient of variation (CV)0.4234403
Kurtosis-0.98559999
Mean284.9
Median Absolute Deviation (MAD)95
Skewness-0.082819177
Sum14245
Variance14553.561
MonotonicityNot monotonic
2025-06-03T16:25:12.471776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
285 4
 
8.0%
145 2
 
4.0%
345 2
 
4.0%
85 2
 
4.0%
225 2
 
4.0%
265 2
 
4.0%
235 2
 
4.0%
125 2
 
4.0%
255 2
 
4.0%
405 2
 
4.0%
Other values (26) 28
56.0%
ValueCountFrequency (%)
75 1
2.0%
85 2
4.0%
95 1
2.0%
105 1
2.0%
115 1
2.0%
125 2
4.0%
135 1
2.0%
145 2
4.0%
195 1
2.0%
205 1
2.0%
ValueCountFrequency (%)
485 2
4.0%
475 1
2.0%
465 1
2.0%
455 1
2.0%
445 1
2.0%
435 1
2.0%
425 1
2.0%
415 1
2.0%
410 1
2.0%
405 2
4.0%

avg_heart_rate
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.28
Minimum65
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:12.556923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile66
Q168.25
median71
Q373
95-th percentile78
Maximum80
Range15
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation3.8968328
Coefficient of variation (CV)0.054669372
Kurtosis-0.50942197
Mean71.28
Median Absolute Deviation (MAD)2
Skewness0.44183326
Sum3564
Variance15.185306
MonotonicityNot monotonic
2025-06-03T16:25:12.646784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
71 7
14.0%
73 5
10.0%
72 5
10.0%
70 5
10.0%
68 4
8.0%
69 4
8.0%
66 4
8.0%
67 3
 
6.0%
78 3
 
6.0%
75 2
 
4.0%
Other values (5) 8
16.0%
ValueCountFrequency (%)
65 2
 
4.0%
66 4
8.0%
67 3
6.0%
68 4
8.0%
69 4
8.0%
70 5
10.0%
71 7
14.0%
72 5
10.0%
73 5
10.0%
75 2
 
4.0%
ValueCountFrequency (%)
80 1
 
2.0%
79 1
 
2.0%
78 3
6.0%
77 2
 
4.0%
76 2
 
4.0%
75 2
 
4.0%
73 5
10.0%
72 5
10.0%
71 7
14.0%
70 5
10.0%

max_heart_rate
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.98
Minimum122
Maximum164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:12.745064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile125.45
Q1140.25
median147.5
Q3155.75
95-th percentile162
Maximum164
Range42
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation11.662761
Coefficient of variation (CV)0.079892871
Kurtosis-0.74999582
Mean145.98
Median Absolute Deviation (MAD)8
Skewness-0.43864752
Sum7299
Variance136.02
MonotonicityNot monotonic
2025-06-03T16:25:12.861482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
145 3
 
6.0%
148 3
 
6.0%
158 2
 
4.0%
157 2
 
4.0%
152 2
 
4.0%
162 2
 
4.0%
143 2
 
4.0%
159 2
 
4.0%
128 2
 
4.0%
151 2
 
4.0%
Other values (25) 28
56.0%
ValueCountFrequency (%)
122 1
2.0%
124 1
2.0%
125 1
2.0%
126 1
2.0%
128 2
4.0%
129 1
2.0%
130 1
2.0%
131 1
2.0%
132 1
2.0%
133 1
2.0%
ValueCountFrequency (%)
164 1
2.0%
163 1
2.0%
162 2
4.0%
161 1
2.0%
160 1
2.0%
159 2
4.0%
158 2
4.0%
157 2
4.0%
156 1
2.0%
155 1
2.0%

min_heart_rate
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.92
Minimum53
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:12.967055image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile54.45
Q159
median63.5
Q366
95-th percentile70.55
Maximum72
Range19
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.1501536
Coefficient of variation (CV)0.081852409
Kurtosis-0.90128743
Mean62.92
Median Absolute Deviation (MAD)4.5
Skewness-0.17617254
Sum3146
Variance26.524082
MonotonicityNot monotonic
2025-06-03T16:25:13.061154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
64 5
 
10.0%
62 4
 
8.0%
66 4
 
8.0%
63 4
 
8.0%
65 4
 
8.0%
59 3
 
6.0%
58 3
 
6.0%
70 3
 
6.0%
56 3
 
6.0%
69 3
 
6.0%
Other values (10) 14
28.0%
ValueCountFrequency (%)
53 1
 
2.0%
54 2
4.0%
55 2
4.0%
56 3
6.0%
57 1
 
2.0%
58 3
6.0%
59 3
6.0%
60 1
 
2.0%
61 1
 
2.0%
62 4
8.0%
ValueCountFrequency (%)
72 1
 
2.0%
71 2
 
4.0%
70 3
6.0%
69 3
6.0%
68 2
 
4.0%
67 1
 
2.0%
66 4
8.0%
65 4
8.0%
64 5
10.0%
63 4
8.0%

sleep_hours_total
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.836
Minimum43.4
Maximum58.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:13.156516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum43.4
5-th percentile45.115
Q151.8
median53.9
Q356
95-th percentile57.785
Maximum58.1
Range14.7
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation4.1526859
Coefficient of variation (CV)0.078595766
Kurtosis-0.33145254
Mean52.836
Median Absolute Deviation (MAD)2.1
Skewness-0.84906731
Sum2641.8
Variance17.2448
MonotonicityNot monotonic
2025-06-03T16:25:13.246836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
56 5
10.0%
53.9 5
10.0%
54.6 5
10.0%
53.2 4
 
8.0%
52.5 4
 
8.0%
57.4 4
 
8.0%
45.5 3
 
6.0%
56.7 3
 
6.0%
51.8 3
 
6.0%
58.1 3
 
6.0%
Other values (9) 11
22.0%
ValueCountFrequency (%)
43.4 1
 
2.0%
44.1 1
 
2.0%
44.8 1
 
2.0%
45.5 3
6.0%
46.2 1
 
2.0%
46.9 1
 
2.0%
47.6 2
4.0%
49 1
 
2.0%
50.4 1
 
2.0%
51.8 3
6.0%
ValueCountFrequency (%)
58.1 3
6.0%
57.4 4
8.0%
56.7 3
6.0%
56 5
10.0%
55.3 2
 
4.0%
54.6 5
10.0%
53.9 5
10.0%
53.2 4
8.0%
52.5 4
8.0%
51.8 3
6.0%

move_minutes
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.7
Minimum105
Maximum665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:13.344218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile126.75
Q1302.5
median410
Q3561.25
95-th percentile645.5
Maximum665
Range560
Interquartile range (IQR)258.75

Descriptive statistics

Standard deviation167.63971
Coefficient of variation (CV)0.41321101
Kurtosis-1.0129387
Mean405.7
Median Absolute Deviation (MAD)135
Skewness-0.18917519
Sum20285
Variance28103.071
MonotonicityNot monotonic
2025-06-03T16:25:13.444667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
580 3
 
6.0%
410 3
 
6.0%
340 2
 
4.0%
210 2
 
4.0%
395 2
 
4.0%
325 2
 
4.0%
180 2
 
4.0%
120 2
 
4.0%
385 1
 
2.0%
640 1
 
2.0%
Other values (30) 30
60.0%
ValueCountFrequency (%)
105 1
2.0%
120 2
4.0%
135 1
2.0%
150 1
2.0%
165 1
2.0%
180 2
4.0%
195 1
2.0%
210 2
4.0%
280 1
2.0%
295 1
2.0%
ValueCountFrequency (%)
665 1
 
2.0%
660 1
 
2.0%
650 1
 
2.0%
640 1
 
2.0%
635 1
 
2.0%
625 1
 
2.0%
615 1
 
2.0%
610 1
 
2.0%
595 1
 
2.0%
580 3
6.0%

exercise_sessions
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.76
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:13.536109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5327713
Coefficient of variation (CV)0.40765193
Kurtosis-0.80613505
Mean3.76
Median Absolute Deviation (MAD)1
Skewness-0.21501522
Sum188
Variance2.3493878
MonotonicityNot monotonic
2025-06-03T16:25:13.615901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 14
28.0%
3 9
18.0%
6 8
16.0%
5 8
16.0%
2 6
12.0%
1 5
 
10.0%
ValueCountFrequency (%)
1 5
 
10.0%
2 6
12.0%
3 9
18.0%
4 14
28.0%
5 8
16.0%
6 8
16.0%
ValueCountFrequency (%)
6 8
16.0%
5 8
16.0%
4 14
28.0%
3 9
18.0%
2 6
12.0%
1 5
 
10.0%

cycling_distance_km
Real number (ℝ)

High correlation  Zeros 

Distinct32
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.298
Minimum0
Maximum24.3
Zeros17
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:13.710129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.8
Q317.65
95-th percentile22.705
Maximum24.3
Range24.3
Interquartile range (IQR)17.65

Descriptive statistics

Standard deviation8.4456362
Coefficient of variation (CV)0.82012393
Kurtosis-1.4272725
Mean10.298
Median Absolute Deviation (MAD)7.8
Skewness-0.036302126
Sum514.9
Variance71.328771
MonotonicityNot monotonic
2025-06-03T16:25:13.814767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 17
34.0%
18.2 2
 
4.0%
14.2 2
 
4.0%
12.5 1
 
2.0%
8.4 1
 
2.0%
22.1 1
 
2.0%
19.8 1
 
2.0%
8.6 1
 
2.0%
16.4 1
 
2.0%
12.8 1
 
2.0%
Other values (22) 22
44.0%
ValueCountFrequency (%)
0 17
34.0%
8.2 1
 
2.0%
8.4 1
 
2.0%
8.6 1
 
2.0%
8.8 1
 
2.0%
9.2 1
 
2.0%
9.8 1
 
2.0%
10.8 1
 
2.0%
11.2 1
 
2.0%
12.4 1
 
2.0%
ValueCountFrequency (%)
24.3 1
2.0%
23.8 1
2.0%
23.2 1
2.0%
22.1 1
2.0%
21.8 1
2.0%
21.2 1
2.0%
20.6 1
2.0%
19.8 1
2.0%
19.4 1
2.0%
18.6 1
2.0%

running_distance_km
Real number (ℝ)

High correlation  Zeros 

Distinct40
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.494
Minimum0
Maximum19.8
Zeros3
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:13.919940image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.36
Q16.25
median9.3
Q313.55
95-th percentile18.765
Maximum19.8
Range19.8
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation5.6488182
Coefficient of variation (CV)0.59498822
Kurtosis-0.86233664
Mean9.494
Median Absolute Deviation (MAD)3.7
Skewness0.031713608
Sum474.7
Variance31.909147
MonotonicityNot monotonic
2025-06-03T16:25:14.035697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 3
 
6.0%
3.2 2
 
4.0%
6.8 2
 
4.0%
15.6 2
 
4.0%
8.8 2
 
4.0%
9.8 2
 
4.0%
8.2 2
 
4.0%
2.1 2
 
4.0%
12.6 2
 
4.0%
9.5 1
 
2.0%
Other values (30) 30
60.0%
ValueCountFrequency (%)
0 3
6.0%
0.8 1
 
2.0%
1.4 1
 
2.0%
1.8 1
 
2.0%
2.1 2
4.0%
2.8 1
 
2.0%
3.2 2
4.0%
5.4 1
 
2.0%
6.2 1
 
2.0%
6.4 1
 
2.0%
ValueCountFrequency (%)
19.8 1
2.0%
19.2 1
2.0%
18.9 1
2.0%
18.6 1
2.0%
17.8 1
2.0%
17.2 1
2.0%
16.8 1
2.0%
16.2 1
2.0%
15.6 2
4.0%
15.3 1
2.0%

walking_distance_km
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.198
Minimum8.6
Maximum24.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:14.159370image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.6
5-th percentile9.68
Q112
median13.15
Q315.2
95-th percentile22.4
Maximum24.4
Range15.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation3.7245183
Coefficient of variation (CV)0.26232697
Kurtosis1.0220145
Mean14.198
Median Absolute Deviation (MAD)1.5
Skewness1.2066549
Sum709.9
Variance13.872037
MonotonicityNot monotonic
2025-06-03T16:25:14.282951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
13 3
 
6.0%
20.5 2
 
4.0%
22.4 2
 
4.0%
11 2
 
4.0%
12 2
 
4.0%
13.1 2
 
4.0%
12.5 2
 
4.0%
15.2 2
 
4.0%
13.9 2
 
4.0%
10 1
 
2.0%
Other values (30) 30
60.0%
ValueCountFrequency (%)
8.6 1
2.0%
9 1
2.0%
9.5 1
2.0%
9.9 1
2.0%
10 1
2.0%
10.5 1
2.0%
11 2
4.0%
11.1 1
2.0%
11.6 1
2.0%
11.7 1
2.0%
ValueCountFrequency (%)
24.4 1
2.0%
22.6 1
2.0%
22.4 2
4.0%
21.9 1
2.0%
20.5 2
4.0%
16.6 1
2.0%
16.2 1
2.0%
16 1
2.0%
15.7 1
2.0%
15.5 1
2.0%

floors_climbed
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.6
Minimum6
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:14.404591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7.45
Q122.5
median31.5
Q340.75
95-th percentile48.55
Maximum52
Range46
Interquartile range (IQR)18.25

Descriptive statistics

Standard deviation13.416408
Coefficient of variation (CV)0.45325702
Kurtosis-0.89047336
Mean29.6
Median Absolute Deviation (MAD)9.5
Skewness-0.33236793
Sum1480
Variance180
MonotonicityNot monotonic
2025-06-03T16:25:14.524645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
28 3
 
6.0%
42 2
 
4.0%
12 2
 
4.0%
35 2
 
4.0%
8 2
 
4.0%
32 2
 
4.0%
25 2
 
4.0%
10 2
 
4.0%
6 2
 
4.0%
33 2
 
4.0%
Other values (25) 29
58.0%
ValueCountFrequency (%)
6 2
4.0%
7 1
2.0%
8 2
4.0%
9 1
2.0%
10 2
4.0%
11 1
2.0%
12 2
4.0%
21 1
2.0%
22 1
2.0%
24 2
4.0%
ValueCountFrequency (%)
52 1
2.0%
51 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 2
4.0%

sedentary_minutes
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4286.8
Minimum2840
Maximum6320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:14.681592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2840
5-th percentile2933.5
Q13390
median4180
Q34520
95-th percentile6180
Maximum6320
Range3480
Interquartile range (IQR)1130

Descriptive statistics

Standard deviation1035.9959
Coefficient of variation (CV)0.24167115
Kurtosis-0.63751562
Mean4286.8
Median Absolute Deviation (MAD)760
Skewness0.62202198
Sum214340
Variance1073287.5
MonotonicityNot monotonic
2025-06-03T16:25:14.802383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3980 3
 
6.0%
3380 3
 
6.0%
4520 3
 
6.0%
4320 2
 
4.0%
5840 2
 
4.0%
3420 2
 
4.0%
3180 2
 
4.0%
4180 2
 
4.0%
6180 2
 
4.0%
4120 2
 
4.0%
Other values (24) 27
54.0%
ValueCountFrequency (%)
2840 1
 
2.0%
2880 1
 
2.0%
2920 1
 
2.0%
2950 1
 
2.0%
3060 1
 
2.0%
3080 1
 
2.0%
3180 2
4.0%
3200 1
 
2.0%
3280 1
 
2.0%
3380 3
6.0%
ValueCountFrequency (%)
6320 1
 
2.0%
6280 1
 
2.0%
6180 2
4.0%
6080 1
 
2.0%
5920 1
 
2.0%
5840 2
4.0%
5720 1
 
2.0%
5680 2
4.0%
4520 3
6.0%
4480 2
4.0%
Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2025-06-03T16:25:15.106778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length21.56
Min length7

Characters and Unicode

Total characters1078
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)82.0%

Sample

1st rowRunning,Gym,Cycling
2nd rowRunning,Swimming,Yoga
3rd rowWalking,Gym
4th rowRunning,Dance,Pilates
5th rowWalking
ValueCountFrequency (%)
running,swimming,cycling 3
 
4.8%
walking,gym 2
 
3.2%
walking 2
 
3.2%
running,gym,cycling 2
 
3.2%
work,walking 2
 
3.2%
work 2
 
3.2%
mgmt 2
 
3.2%
cycling,gym,running 1
 
1.6%
running,dance,pilates 1
 
1.6%
cycling,design 1
 
1.6%
Other values (44) 44
71.0%
2025-06-03T16:25:15.486437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 194
18.0%
i 131
12.2%
g 113
 
10.5%
, 87
 
8.1%
u 49
 
4.5%
l 49
 
4.5%
R 40
 
3.7%
a 40
 
3.7%
m 40
 
3.7%
c 31
 
2.9%
Other values (31) 304
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1078
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 194
18.0%
i 131
12.2%
g 113
 
10.5%
, 87
 
8.1%
u 49
 
4.5%
l 49
 
4.5%
R 40
 
3.7%
a 40
 
3.7%
m 40
 
3.7%
c 31
 
2.9%
Other values (31) 304
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1078
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 194
18.0%
i 131
12.2%
g 113
 
10.5%
, 87
 
8.1%
u 49
 
4.5%
l 49
 
4.5%
R 40
 
3.7%
a 40
 
3.7%
m 40
 
3.7%
c 31
 
2.9%
Other values (31) 304
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1078
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 194
18.0%
i 131
12.2%
g 113
 
10.5%
, 87
 
8.1%
u 49
 
4.5%
l 49
 
4.5%
R 40
 
3.7%
a 40
 
3.7%
m 40
 
3.7%
c 31
 
2.9%
Other values (31) 304
28.2%

avg_pace_min_per_km
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.916
Minimum4.4
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:15.625637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.4
5-th percentile4.545
Q14.95
median5.8
Q36.275
95-th percentile7.855
Maximum8.2
Range3.8
Interquartile range (IQR)1.325

Descriptive statistics

Standard deviation1.0477108
Coefficient of variation (CV)0.17709784
Kurtosis-0.42869159
Mean5.916
Median Absolute Deviation (MAD)0.6
Skewness0.64414906
Sum295.8
Variance1.097698
MonotonicityNot monotonic
2025-06-03T16:25:15.738719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4.9 4
 
8.0%
5.8 3
 
6.0%
6.2 3
 
6.0%
5.5 3
 
6.0%
5.9 3
 
6.0%
4.8 3
 
6.0%
6 3
 
6.0%
6.1 2
 
4.0%
5.7 2
 
4.0%
6.3 2
 
4.0%
Other values (19) 22
44.0%
ValueCountFrequency (%)
4.4 1
 
2.0%
4.5 2
4.0%
4.6 1
 
2.0%
4.7 2
4.0%
4.8 3
6.0%
4.9 4
8.0%
5.1 1
 
2.0%
5.2 1
 
2.0%
5.4 1
 
2.0%
5.5 3
6.0%
ValueCountFrequency (%)
8.2 1
2.0%
8.1 1
2.0%
7.9 1
2.0%
7.8 1
2.0%
7.7 1
2.0%
7.6 1
2.0%
7.5 1
2.0%
7.4 1
2.0%
7.3 1
2.0%
7.2 1
2.0%

stress_level_avg
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.326
Minimum1.6
Maximum5.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2025-06-03T16:25:15.852725image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile1.745
Q12.325
median3.15
Q33.675
95-th percentile5.555
Maximum5.8
Range4.2
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation1.2476607
Coefficient of variation (CV)0.37512347
Kurtosis-0.63824809
Mean3.326
Median Absolute Deviation (MAD)0.7
Skewness0.64606486
Sum166.3
Variance1.5566571
MonotonicityNot monotonic
2025-06-03T16:25:15.976206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3.2 3
 
6.0%
2.1 3
 
6.0%
2.8 3
 
6.0%
3.4 3
 
6.0%
1.8 2
 
4.0%
3.1 2
 
4.0%
5.5 2
 
4.0%
2.9 2
 
4.0%
2 2
 
4.0%
3.3 2
 
4.0%
Other values (21) 26
52.0%
ValueCountFrequency (%)
1.6 2
4.0%
1.7 1
 
2.0%
1.8 2
4.0%
1.9 1
 
2.0%
2 2
4.0%
2.1 3
6.0%
2.2 1
 
2.0%
2.3 1
 
2.0%
2.4 1
 
2.0%
2.5 1
 
2.0%
ValueCountFrequency (%)
5.8 1
2.0%
5.7 1
2.0%
5.6 1
2.0%
5.5 2
4.0%
5.4 1
2.0%
5.3 1
2.0%
5.2 1
2.0%
5.1 2
4.0%
4.8 1
2.0%
3.8 1
2.0%

Interactions

2025-06-03T16:25:09.435673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:53.724645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:54.640363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:55.810724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:56.678943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:57.573755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:58.539798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:59.670202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:00.573451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:01.458620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-03T16:25:03.393124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2025-06-03T16:25:00.306601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:01.208687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:02.290969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:03.139816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:03.990200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:05.092720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:06.073992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:06.999257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:07.923088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:09.142737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:10.204339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:54.422788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:55.578905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:56.478143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:57.368456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:58.324098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:59.456547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:00.358866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:01.257166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:02.340157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:03.192395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:04.040293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:05.140367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:06.152705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:07.048641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:07.993028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:09.215394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:10.263354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:54.472745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:55.643510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:56.524108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:57.412009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:58.375109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:59.508659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:00.406567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:01.312022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:02.390150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:03.243236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:04.073652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:05.210687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:06.212296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:07.099065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:08.046259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:09.270871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:10.317258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:54.524796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:55.695617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:56.578147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:57.467383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:58.424795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:59.562680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:00.471795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:01.357188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:02.440108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:03.292726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:04.138222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:05.260155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:06.279091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:07.146839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:08.107011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:09.329817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:10.367085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:54.589400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:55.757859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:56.628980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:57.527311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:58.494608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:24:59.608729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:00.523285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:01.409065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:02.490156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:03.341363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:04.189398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:05.340323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:06.332462image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:07.195825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:08.168244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-03T16:25:09.388366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-03T16:25:16.079480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
avg_heart_rateavg_pace_min_per_kmcycling_distance_kmexercise_sessionsfloors_climbedmax_heart_ratemin_heart_ratemove_minutesrunning_distance_kmsedentary_minutessleep_hours_totalstress_level_avgtotal_active_minutestotal_calories_burnedtotal_distance_kmtotal_stepswalking_distance_km
avg_heart_rate1.0000.912-0.840-0.900-0.919-0.9120.948-0.912-0.9270.918-0.9210.918-0.912-0.910-0.909-0.9100.479
avg_pace_min_per_km0.9121.000-0.920-0.972-0.992-0.9890.920-0.996-0.9690.986-0.8780.992-0.997-0.995-0.990-0.9900.523
cycling_distance_km-0.840-0.9201.0000.9210.9350.930-0.8140.9260.840-0.9100.787-0.9320.9240.9190.9120.912-0.742
exercise_sessions-0.900-0.9720.9211.0000.9740.978-0.9230.9770.949-0.9800.878-0.9770.9780.9750.9800.980-0.522
floors_climbed-0.919-0.9920.9350.9741.0000.993-0.9100.9930.964-0.9840.887-0.9960.9940.9900.9880.988-0.553
max_heart_rate-0.912-0.9890.9300.9780.9931.000-0.9100.9900.959-0.9830.883-0.9900.9920.9880.9860.986-0.557
min_heart_rate0.9480.920-0.814-0.923-0.910-0.9101.000-0.924-0.9450.944-0.9210.920-0.923-0.927-0.938-0.9370.376
move_minutes-0.912-0.9960.9260.9770.9930.990-0.9241.0000.971-0.9910.886-0.9941.0000.9970.9940.994-0.527
running_distance_km-0.927-0.9690.8400.9490.9640.959-0.9450.9711.000-0.9730.932-0.9660.9720.9730.9740.974-0.432
sedentary_minutes0.9180.986-0.910-0.980-0.984-0.9830.944-0.991-0.9731.000-0.8940.990-0.991-0.991-0.995-0.9960.493
sleep_hours_total-0.921-0.8780.7870.8780.8870.883-0.9210.8860.932-0.8941.000-0.8880.8860.8880.8880.888-0.440
stress_level_avg0.9180.992-0.932-0.977-0.996-0.9900.920-0.994-0.9660.990-0.8881.000-0.994-0.990-0.991-0.9910.544
total_active_minutes-0.912-0.9970.9240.9780.9940.992-0.9231.0000.972-0.9910.886-0.9941.0000.9970.9940.994-0.525
total_calories_burned-0.910-0.9950.9190.9750.9900.988-0.9270.9970.973-0.9910.888-0.9900.9971.0000.9940.994-0.515
total_distance_km-0.909-0.9900.9120.9800.9880.986-0.9380.9940.974-0.9950.888-0.9910.9940.9941.0001.000-0.486
total_steps-0.910-0.9900.9120.9800.9880.986-0.9370.9940.974-0.9960.888-0.9910.9940.9941.0001.000-0.485
walking_distance_km0.4790.523-0.742-0.522-0.553-0.5570.376-0.527-0.4320.493-0.4400.544-0.525-0.515-0.486-0.4851.000

Missing values

2025-06-03T16:25:10.477295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-03T16:25:10.632786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_idweek_start_datetotal_stepstotal_distance_kmtotal_calories_burnedtotal_active_minutesavg_heart_ratemax_heart_ratemin_heart_ratesleep_hours_totalmove_minutesexercise_sessionscycling_distance_kmrunning_distance_kmwalking_distance_kmfloors_climbedsedentary_minutesworkout_typesavg_pace_min_per_kmstress_level_avg
0USR0012023-10-025284037.22480285721455852.5420412.58.716.0283980Running,Gym,Cycling5.83.2
1USR0022023-10-026872048.12890410661585456.0580618.215.314.6423200Running,Swimming,Yoga4.92.1
2USR0032023-10-022834019.81650145781326845.521020.03.216.6125840Walking,Gym6.24.8
3USR0042023-10-026158043.12650345731526254.648558.412.821.9353420Running,Dance,Pilates5.52.8
4USR0052023-10-021892013.2128085751256947.612010.00.013.286180Walking7.85.5
5USR0062023-10-027248050.73120465681625657.4640622.118.610.0482950Running,Swimming,Cycling4.61.8
6USR0072023-10-024856034.02280265711486451.8385414.26.813.0324120Cycling,Gym,Running5.93.1
7USR0082023-10-024283030.02140235691436155.334030.09.520.5254380Running,Yoga,Walking6.13.5
8USR0092023-10-022468017.31420125771287043.418020.02.115.2105920Walking,Gym7.45.2
9USR0102023-10-026934048.53050425671595556.7610619.816.212.5443180Running,Swimming,Cycling4.82.0
user_idweek_start_datetotal_stepstotal_distance_kmtotal_calories_burnedtotal_active_minutesavg_heart_ratemax_heart_ratemin_heart_ratesleep_hours_totalmove_minutesexercise_sessionscycling_distance_kmrunning_distance_kmwalking_distance_kmfloors_climbedsedentary_minutesworkout_typesavg_pace_min_per_kmstress_level_avg
40USR0412023-10-025168036.22520305711496453.2440413.810.412.0344120Running,Automotive,Cycling5.62.9
41USR0422023-10-025284037.02480285701486254.641040.012.624.4303980Running,Nutrition,Walking5.83.2
42USR0432023-10-022768019.41520145761336944.121020.03.216.2125680Walking,Production7.35.1
43USR0442023-10-026482045.42890405681575956.0580518.215.611.6413380Running,Swimming,Therapy4.92.1
44USR0452023-10-024128028.92040225721416553.932538.86.213.9244520Running,Web Dev,Cycling6.33.6
45USR0462023-10-025468038.32620335691516253.2480414.210.813.3363920Running,Business,Cycling5.52.8
46USR0472023-10-022184015.31380105781287145.515010.01.413.986080Walking,Plant Mgmt7.75.5
47USR0482023-10-026984048.93120445661605657.4635621.217.89.9473080Running,Swimming,Science4.71.8
48USR0492023-10-024928034.52380285711486452.5410413.69.211.7334180Running,Marine Eng,Cycling5.73.0
49USR0502023-10-024568032.02240255711456353.936530.09.422.6284320Running,Museum,Walking6.03.4